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Senior LLMOps, RAG & infrastructure

Adlin-Science
Paris, France
1 day ago
Apply on www.welcometothejungle.com
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Working hours
Regular working hours
Job source
www.welcometothejungle.com

Tech stack

Application Programming Interfaces (APIs) Continuous Integration Linux DevOps Information Retrieval Python (Programming Language) Machine Learning Language Modeling Open Source Technology Regression Testing Prometheus Search Technologies
+25 more
Management of Software Versions AI Infrastructure QLoRA Data Processing Qdrant Pytorch Retrieval-Augmented Generation Transfer Learning Delivery Pipeline Large Language Models Grafana Technical Debt Generative AI Backend Discretization Pgvector Containerization Kubernetes Milvus Machine Learning Operations TensorRT Terraform OpenSearch Docker Vulnerability Analysis

Job description

  • You are the LLM technical referent within the Data team and work closely with the Backend, DevOps, Security, Product and domain expert teams.
  • You design the components specific to the LLM chain and integrate them into the existing Adlin architecture.
  • You do not redefine infrastructure, backend or security standards on your own: you rely on the choices, tools and constraints defined with the responsible teams.
  • You prioritize installable, auditable and maintainable solutions, with no dependency on an external API in production., * Select, test and qualify open models compatible with local deployment and licensing, confidentiality and redistribution constraints.
  • Define a modular architecture allowing the model, inference engine or RAG strategy to be changed without excessive coupling.
  • Implement local RAG: embeddings, lexical and vector search, hybrid search, reranking, metadata filters and citation management.
  • Deploy and operate local inference engines such as vLLM, llama.cpp, TGI, TensorRT-LLM, Triton or equivalent solutions depending on the need
  • Optimize latency, throughput, memory consumption and stability through quantization, continuous batching, KV-cache management, parallelism and appropriate format choices.
  • Set up load, resilience and non-regression tests before each production release.
  • Build pipelines for packaging, versioning, validation, promotion and rollback of models, prompts, embeddings, indexes and configurations
  • Define a controlled process for importing models and dependencies into the secure environment: signed artifacts, integrity checks, inventory, vulnerability scans and approval procedure.
  • Containerize components and integrate them with the CI/CD and orchestration tools selected by the Backend / DevOps teams
  • Produce runbooks, incident procedures, architecture files, test evidence and the elements required for security and quality reviews.

Requirements

  • Hands-on experience deploying and operating LLMs or NLP/ML systems under strong performance constraints.
  • Excellent command of Python, PyTorch, the Transformers ecosystem and the architectural principles of language models.
  • Mastery of at least one high-performance inference engine and experience with quantization and GPU sizing.
  • Practical experience with RAG architectures, embeddings, hybrid search, reranking and evaluation of generative systems.
  • Strong Docker, Linux, CI/CD, observability and artifact management skills in controlled environments.
  • Security culture: secrets, access control, encryption, isolation, software supply chain and sensitive data processing.

  • Ability to document, explain trade-offs and work with multiple teams without creating isolated technical debt. Nice to have

  • Kubernetes, Terraform, MLflow, Prometheus/Grafana, OpenSearch and locally deployable vector databases such as Qdrant, Milvus or pgvector.
  • PEFT/LoRA/QLoRA fine-tuning, distillation, distributed training and preparation of supervised datasets.
  • Knowledge of healthcare sector constraints, GDPR, sensitive data and regulated environments.
  • Experience with air-gapped, on-premise, appliance or edge environments, including controlled import procedures.
  • Open-source contribution or experience reading and critically evaluating scientific papers., * Autonomy and decision-making: you know how to scope your work and move forward even when things are not fully defined.
  • Proactive mindset and critical thinking.
  • Comfortable in dynamic, evolving environments: you thrive when processes are still being shaped.
  • Open and constructive communication: you can break down technical concepts for non-technical audiences, share knowledge freely and foster collaborative dialogue., * Master’s degree or PhD in computer science, AI, machine learning or a related field, or equivalent experience.
  • Significant experience in ML Engineering, MLOps or AI infrastructure, with at least one LLM deployment actually operated in production.
  • Hands-on profile, able to prototype, code, benchmark, diagnose and industrialize
  • Rigor, autonomy, team spirit and ability to work in a context where traceability and quality come first.
  • Fluent technical English.

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This job is hosted externally. Click below to view the full posting and apply.

Apply on www.welcometothejungle.com
Prepare application

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